Graph Theoretical Analysis of Interictal EEG Data in Epilepsy Patients during Epileptiform Discharge and Non-discharge

Carpels, Steven M.A.; Yamamoto, Yusuke; Mizuno‐Matsumoto, Yuko

doi:10.5057/ijae.ijae-d-20-00026

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…The recommended lower cut-off point by several adult [174, 175] and infantile studies [7, 33, 62, 98] when performing graph theory analysis is between 0.1-0.5. It should be mentioned that thresholding could be skipped when calculating for example weighted graphs and measures, such as the weighted degree [98, 171].…”

Section: Processingmentioning

confidence: 99%

EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

Tsolisou

2023

Brain Science Advances

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Over the last decades, infantile brain networks have received increased scientific attention due to the elevated need to understand better the maturational processes of the human brain and the early forms of neural abnormalities. Electroencephalography (EEG) is becoming a popular tool for the investigation of functional connectivity (FC) of the immature brain, as it is easily applied in awake, non-sedated infants. However, there are still no universally accepted standards regarding the preprocessing and processing analyses which address the peculiarities of infantile EEG data, resulting in comparability difficulties between different studies. Nevertheless, during the last few years, there is a growing effort in overcoming these issues, with the creation of age-appropriate pipelines. Although FC in infants has been mostly measured via linear metrics and particularly coherence analysis, non-linear methods, such as cross-frequency-coupling (CFC), may be more valuable for the investigation of network communication and early network development. Additionally, graph theory analysis often accompanies linear and non-linear FC computation offering a more comprehensive understanding of the infantile network architecture. The current review attempts to gather the basic information on the preprocessing and processing techniques that are usually employed by infantile FC studies, while providing guidelines for future studies.

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Section: Processingmentioning

confidence: 99%

EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

Tsolisou

2023

Brain Science Advances

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“…In addition, prior studies found that edge densities of 0.1-0.4 were appropriate for GCC, CPL, and SW measures (Mehraram et al, 2020;Carpels et al, 2021). The thresholded weighted matrix was then normalized by dividing each element by the maximum connectivity value to avoid the influence of network strength on the GT measures (Onnela et al, 2005;Antoniou and Tsompa, 2008;Mehraram et al, 2020;Carpels et al, 2021). This normalized matrix was used to calculate GCC, and it was also converted into a distance matrix to calculate CPL.…”

Section: Graph Theory Metricsmentioning

confidence: 99%

Evolution of Cortical Functional Networks in Healthy Infants

Goetz

et al. 2022

Front. Netw. Physiol.

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During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0–2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21–24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

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Spatial characterization of functional neural activity during lower limb motion through functional connectivity

Espinoza-Valdez,

Quiroz-Compean,

González-Garrido

et al. 2024

Biocybernetics and Biomedical Engineering

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Graph Theoretical Analysis of Interictal EEG Data in Epilepsy Patients during Epileptiform Discharge and Non-discharge

Cited by 4 publications

References 47 publications

EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

EEG connectivity analysis in infants: A Beginner’s Guide on Preprocessing and Processing Techniques

Evolution of Cortical Functional Networks in Healthy Infants

Spatial characterization of functional neural activity during lower limb motion through functional connectivity

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